Hiring that measures how people think, not what they memorize.

82% of employers now run resumes through AI filters, and nearly half of candidates write them with AI. The signal dies in the wash. TalentOS measures the one thing AI can't fake: how you reason when the problem is real.

RESUMEBUILDER, 2024

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Incident
Architecture
Schema
Data
Console
Logs
Thread

Incident

SEV-2 · Nightly revenue rollup double-counts orders

At 02:15 UTC, Finance flagged an unexpected spike in nightly revenue. The rollup job completed successfully, but metrics are inflated. Identify the root cause and propose a fix with validation steps.

Services 8Tables 24Updated 2h ago

DetailsCustomer impactTimelineArtifacts

Summary

Revenue rollup shows ~2.1x inflation starting yesterday 02:00 UTC.

Impact

· Finance reporting inaccurate
· ~$120K over-reported
· Affecting daily dashboards

SQL Console

ConnectedRead replica▶

1SELECT
2  o.order_date,
3  COUNT(*) AS orders,
4  SUM(o.amount) AS revenue
5FROM orders o
6JOIN order_items i ON i.order_id = o.id
7GROUP BY 1 ORDER BY 1 DESC;

✦ AI Teammate

Evaluated

I reviewed the incident details and scanned recent schema and job changes. Here's what stands out.

· Likely cause
orders is joined to order_items without deduplicating items. Orders with multiple items are counted multiple times.

· Evidence
– Join pattern inflates COUNT(*)
– Rollup job changed 26h ago
– Spike aligns with new join path

· Next step
Deduplicate orders before aggregation. I'll draft a query and validation checks.

Ask a follow-up…↗

AI is expected, not cheating · No surveillance, ever · Your reasoning is the signal

The state of hiring · 2026

The AI hiring doom loop.

AI writes the applications. AI filters the applications. Both sides lose the signal, and the people inside the loop feel it.

53%

of candidates were ghosted in the past year. A three-year peak.

FORTUNE, MAR 2026

84.7%

of candidates don't know whether an AI is reading their resume.

ENHANCV, JUN 2026 · N=1,066

31%

have abandoned a hiring process rather than face a one-sided AI interview. 36% among the youngest.

ENHANCV, JUN 2026

The TalentOS loop

Post a role. The rest generates itself.

1.0

Post a role →

Your JD becomes a complete incident scene: schema, logs, data, team thread. Generated, frozen, comparable.

Create a new assessment

Role

Staff Data Engineer

Team (optional)

Data Platform

Job description

We're looking for a Staff Data Engineer to debug and improve our revenue pipeline. Strong SQL, data modeling, and production troubleshooting experience required.

Generate assessment ✦

Incident

Generated · frozen for this role

SEV-2 · Nightly revenue rollup double-counts orders

Schema, sample data, logs, and a team thread. Every candidate gets this same scene.

2.0

Candidates reason live →

They investigate for real, with an AI teammate and a real SQL console. The conversation is part of the evidence.

Incident

Generated

SEV-2 · Nightly revenue rollup double-counts orders

At 02:15 UTC, Finance flagged an unexpected spike in nightly revenue. The rollup job completed successfully, but metrics are inflated. Identify the root cause and propose a fix with validation steps.

Services 8Tables 24Updated 2h ago

DetailsCustomer impactTimelineArtifacts

SQL Console

ConnectedRead replica▶

1SELECT
2  o.order_date,
3  COUNT(*) AS orders,
4  SUM(o.amount) AS revenue
5FROM orders o
6JOIN order_items i ON i.order_id = o.id
7GROUP BY 1 ORDER BY 1 DESC;

✦ AI Teammate

Evaluated

I reviewed the incident details and scanned recent schema and job changes. Here's what stands out.

Before you commit: how did you rule out a backfill re-run? The rollup job changed 26h ago, and the new join path is where I'd validate first.

Ask a follow-up…↗

3.0

Read the reasoning →

No black-box score. An evidence review with cited key moments from the transcript, dimension by dimension.

Evidence review

Staff Data Engineer · SEV-2 Revenue Rollup

Recommendation

Strong hire ✓

Key reasoning moments

12:43“The join to order_items can duplicate orders. Let me validate the cardinality.”

View in transcript ↗

18:07“The rollup job change two days ago switched the join path. That matches the spike.”

View in transcript ↗

26:31“I'll deduplicate by order_id before aggregation and re-run the validation.”

View in transcript ↗

Assessment dimensions

Problem framingStrong

Data investigationStrong

Hypothesis qualityStrong

SQL proficiencyStrong

Validation rigorStrong

CommunicationStrong

Summary

Formed accurate hypotheses, validated with data, and proposed a correct fix with clear checks. Strong production intuition and SQL skills.

Dimensions evaluated by AI · Reviewed by hiring team

Any stack. Any seniority. Zero setup.

Fig 1.0 · The loop

Built to a target

~50%*

Designed to cut time-to-signal by ~50%

8h*

Built to surface signal 8 hours of technical panel review can miss

One frozen scenario per role, generated for any stack in minutes. Identical for every candidate

100%

Every candidate receives the reasoning about them. Transparency runs both directions

Zero timers, keystroke logs, or proctoring. Reasoning is the only signal we score

*Projected targets based on assessment design and early internal testing. Full benchmarks published post-launch.

Two sides of the same evidence

For hiring managers

Escape the doom loop.

Read the reasoning, not a score. Cited evidence from a real investigation, and your candidate receives the same reasoning you do.

See how teams hire →

For candidates

Finally, an interview that lets you think.

A real problem, a real console, an AI teammate. You get the full reasoning back, the same reasoning your reviewer reads.

See how you're evaluated →

The founding cohort

TalentOS is being shaped by its first hiring teams and candidates, not launched at them. Every assessment run teaches the system.

You're not a user of a finished product. You're a co-author of a new standard for how people get hired in the AI era.

2 hiring teams shaping the founding standard.

Reserve your place →